Key points
- Oracle ran the Mythos model against its code in March, uncovering serious security vulnerabilities.
- Quarterly security updates now address over 1,400 vulnerabilities across the company’s software portfolio.
- Real Application Testing captures actual workloads to verify that large patches do not break production systems.
Oracle is addressing a surge in software flaws identified by frontier AI models by hardening its code and moving agent controls to the data layer. According to SiliconANGLE, this shift is forcing database providers to treat security as a persistent data-layer problem rather than a peripheral concern.reportedOracle is updating its software more thoroughly because AI tools are finding security problems faster than ever before.
Evidence: reported
Oracle connects AI-driven testing with a new approach to database securityaifleet-siliconangle
The evidence
Juan Loaiza, executive vice president of Oracle Database Technologies, stated that the company gained early access to the Mythos model in March. according to the published report, running this model against Oracle’s codebase revealed numerous serious security issues, which he described as a critical moment requiring immediate action.reported
Evidence: reported
Oracle connects AI-driven testing with a new approach to database securityaifleet-siliconangle
The decision
In response to these findings, Oracle has integrated its response into regular quarterly patches, which have grown significantly larger in scope. Loaiza explained that a recent quarterly update addressed more than 1,400 vulnerabilities across the company’s entire portfolio, necessitating new safeguards for customers.reported
Evidence: reported
Oracle connects AI-driven testing with a new approach to database securityaifleet-siliconangle
What the evidence means
The expansion of patch sizes introduces a risk of regression in complex production environments that cannot tolerate downtime. To mitigate this, Oracle is promoting Real Application Testing, a tool that captures real-world workloads to ensure that new fixes do not disrupt existing applications.
What builders can do with it
Developers must automate their patching processes to keep pace with these larger updates. They can use Real Application Testing to simulate their specific workloads before deploying patches, ensuring that application logic remains intact. Builders should also recognise that traditional application-level security rules are insufficient against AI agents that interact directly with data layers.
Why it matters
- Policy readers should note that security is shifting from application-level enforcement to data-layer controls due to AI agent capabilities.
- The volume of vulnerabilities addressed in single updates now exceeds 1,400, indicating a fundamental change in software maintenance rhythms.
- Real Application Testing is becoming a necessary safeguard to prevent operational disruption when applying large security patches.
Topics: Oracle, AI security, database patching, Mythos model, Real Application Testing
Updated
The strongest doubt
Counterpoint from the sources
Large patches raise fears of regression in systems that cannot fail, complicating the adoption of necessary security fixes.
Ask the article
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Your choice stays on this device. No public tally.Questions readers ask
How has Oracle changed its patching strategy?
Oracle has increased the size of its quarterly patches to address hundreds of AI-discovered vulnerabilities simultaneously.
What tool helps prevent patches from breaking applications?
Real Application Testing captures actual workloads to verify that patches do not negatively affect production systems.
Which AI model did Oracle use to find vulnerabilities?
Oracle used early access to the Mythos model to scan its code for security flaws in March.
Connected stories
Reading receipt
- Oracle ran the Mythos model against its code in March, uncovering serious security vulnerabilities.
- Quarterly security updates now address over 1,400 vulnerabilities across the company’s software portfolio.
- Real Application Testing captures actual workloads to verify that large patches do not break production systems.
Still open: How many of the 1,400 vulnerabilities were specifically found by the Mythos model versus other methods?
Still open: What specific controls are being moved to the data layer to counter AI agents?